{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6037863-2128-40a0-81fa-d8f5b529f82c",
   "metadata": {},
   "source": [
    "# QAOA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1db0a92-7f61-4132-81b9-cf7144f08458",
   "metadata": {},
   "source": [
    "This notebook demonstrates the Classiq performance re. the Quantum Approximate Optimization Algorithm (QAOA), focusing on the Max Clique problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97fc0521-4ba4-44cc-9482-15184ac593c0",
   "metadata": {},
   "source": [
    "## 1. Calling the Built-in QAOA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a990e63e4d7f14",
   "metadata": {},
   "source": [
    "This section calls the built-in QAOA of Classiq, constructing the corresponding quantum model from a combinatorial optimization Pyomo model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c98764d-fa71-4f15-af23-ce9f297015e3",
   "metadata": {},
   "source": [
    "### 1.1. Generating a Pyomo Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b873a49-ac0e-48fa-bd62-0ccaf8087c6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:45:15.707900Z",
     "iopub.status.busy": "2024-05-07T13:45:15.707443Z",
     "iopub.status.idle": "2024-05-07T13:45:16.209308Z",
     "shell.execute_reply": "2024-05-07T13:45:16.208467Z"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import pyomo.environ as pyo\n",
    "\n",
    "np.random.seed(2)\n",
    "\n",
    "\n",
    "def define_max_clique_model(graph):\n",
    "    model = pyo.ConcreteModel()\n",
    "\n",
    "    # each x_i states if node i belongs to the cliques\n",
    "    model.x = pyo.Var(graph.nodes, domain=pyo.Binary)\n",
    "    x_variables = np.array(list(model.x.values()))\n",
    "\n",
    "    # define the complement adjacency matrix as the matrix where 1 exists for each non-existing edge\n",
    "    adjacency_matrix = nx.convert_matrix.to_numpy_array(graph, nonedge=0)\n",
    "    complement_adjacency_matrix = (\n",
    "        1\n",
    "        - nx.convert_matrix.to_numpy_array(graph, nonedge=0)\n",
    "        - np.identity(len(model.x))\n",
    "    )\n",
    "\n",
    "    # constraint that 2 nodes without an edge in the graph cannot be chosen together\n",
    "    model.clique_constraint = pyo.Constraint(\n",
    "        expr=x_variables @ complement_adjacency_matrix @ x_variables == 0\n",
    "    )\n",
    "\n",
    "    # maximize the number of nodes in the chosen clique\n",
    "    model.value = pyo.Objective(expr=sum(x_variables), sense=pyo.maximize)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0edeb4ba-f7f5-4c6c-9a52-f686f01bef42",
   "metadata": {},
   "source": [
    "Setting a specific problem and some hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "363ebca3-7948-447c-9770-f670a805ec57",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:45:16.214732Z",
     "iopub.status.busy": "2024-05-07T13:45:16.213350Z",
     "iopub.status.idle": "2024-05-07T13:45:16.223497Z",
     "shell.execute_reply": "2024-05-07T13:45:16.222940Z"
    }
   },
   "outputs": [],
   "source": [
    "QAOA_NUM_LAYERS = 10\n",
    "NUM_SHOTS = 1e4\n",
    "NUM_QUBITS = 7\n",
    "graph = nx.erdos_renyi_graph(NUM_QUBITS, 0.6, seed=79)\n",
    "max_clique_model = define_max_clique_model(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65d275ad-12f9-4f32-8d36-bb32277fc5ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:45:16.235745Z",
     "iopub.status.busy": "2024-05-07T13:45:16.234645Z",
     "iopub.status.idle": "2024-05-07T13:45:16.239575Z",
     "shell.execute_reply": "2024-05-07T13:45:16.238893Z"
    }
   },
   "outputs": [],
   "source": [
    "# transpilation_options = {\"classiq\": \"custom\", \"qiskit\": 3}\n",
    "transpilation_options = {\"classiq\": \"auto optimize\", \"qiskit\": 1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30aa5869-42e1-46c6-865f-c6be81aea09a",
   "metadata": {},
   "source": [
    "### 1.2 Constructing, Synthesizing, and Running a QAOA Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b7bc9756-cadd-4548-9a43-72de5afe769b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:45:16.246075Z",
     "iopub.status.busy": "2024-05-07T13:45:16.243120Z",
     "iopub.status.idle": "2024-05-07T13:47:51.250613Z",
     "shell.execute_reply": "2024-05-07T13:47:51.249963Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import (\n",
    "    CustomHardwareSettings,\n",
    "    Preferences,\n",
    "    QuantumProgram,\n",
    "    construct_combinatorial_optimization_model,\n",
    "    execute,\n",
    "    set_execution_preferences,\n",
    "    set_preferences,\n",
    "    show,\n",
    "    synthesize,\n",
    ")\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "from classiq.execution import ExecutionPreferences\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=QAOA_NUM_LAYERS)\n",
    "optimizer_config = OptimizerConfig(max_iteration=400, alpha_cvar=1)\n",
    "\n",
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=max_clique_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")\n",
    "\n",
    "\n",
    "execution_preferences = ExecutionPreferences(num_shots=NUM_SHOTS)\n",
    "preferences = Preferences(\n",
    "    custom_hardware_settings=CustomHardwareSettings(basis_gates=[\"cx\", \"u\"]),\n",
    "    transpilation_option=transpilation_options[\"classiq\"],\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, execution_preferences)\n",
    "qmod = set_preferences(qmod, preferences=preferences)\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "res = execute(qprog).result()\n",
    "circuit = QuantumProgram.from_qprog(qprog)\n",
    "depth_classiq = circuit.transpiled_circuit.depth\n",
    "cx_counts_classiq = circuit.transpiled_circuit.count_ops[\"cx\"]\n",
    "classiq_solving_time = res[0].value.time\n",
    "\n",
    "interations_classiq = [\n",
    "    intermediate_result.iteration_number\n",
    "    for intermediate_result in res[0].value.intermediate_results\n",
    "]\n",
    "results_classiq = [\n",
    "    -intermediate_result.mean_all_solutions\n",
    "    for intermediate_result in res[0].value.intermediate_results\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05215b48-ff5a-4087-bb03-7409f6961821",
   "metadata": {},
   "source": [
    "## 2. Comparing to Qiskit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea922d32b6fd7e39",
   "metadata": {},
   "source": [
    "We use qiskit version 0.39.4. \n",
    "\n",
    "Qiskit has no module in which to specify a generic optimization problem; therefore, you have to do the preprocessing and post-processing yourself.\n",
    "Retrieve the Hamiltonian that enters into the VQE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1ae3486-e63a-4a08-b65b-258b73dfc7c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:47:51.253524Z",
     "iopub.status.busy": "2024-05-07T13:47:51.253140Z",
     "iopub.status.idle": "2024-05-07T13:47:51.257476Z",
     "shell.execute_reply": "2024-05-07T13:47:51.256838Z"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "from classiq import Pauli as ClassiqPauli\n",
    "\n",
    "\n",
    "def get_classiq_hamiltonian(execution_result) -> List[List[str]]:\n",
    "    hamiltonian_result = execution_result[1].value\n",
    "    parsed_pauli_list = list()\n",
    "    for pauli_term in hamiltonian_result:\n",
    "        pauli_str = \"\".join(ClassiqPauli(pauli).name for pauli in pauli_term[\"pauli\"])\n",
    "        coefficient = pauli_term[\"coefficient\"]\n",
    "        parsed_pauli_list.append([pauli_str, coefficient])\n",
    "\n",
    "    return parsed_pauli_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e664067-7f8b-4bf0-9675-5f441175744c",
   "metadata": {},
   "source": [
    "Define a function for running QAOA on Qiskit and returning the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6428fb6a-fd59-4fb6-bce0-3f95bbd12d24",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:47:51.259964Z",
     "iopub.status.busy": "2024-05-07T13:47:51.259582Z",
     "iopub.status.idle": "2024-05-07T13:47:54.563069Z",
     "shell.execute_reply": "2024-05-07T13:47:54.562391Z"
    }
   },
   "outputs": [],
   "source": [
    "from qiskit import QuantumCircuit, transpile\n",
    "from qiskit.algorithms.minimum_eigensolvers import QAOA\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "from qiskit.primitives import Estimator, Sampler\n",
    "from qiskit.quantum_info import Pauli, SparsePauliOp, Statevector\n",
    "from qiskit.result import QuasiDistribution\n",
    "\n",
    "\n",
    "def qiskit_qaoa(hamiltonian, qaoa_num_layers, num_qubits):\n",
    "    \"\"\"\n",
    "    Gets a Hamiltonian for QAOA and num of quantum layers, returning the most probable solution and its corresponding cost\n",
    "    as well as intermediate results\n",
    "    \"\"\"\n",
    "    counts = []\n",
    "    values = []\n",
    "\n",
    "    def store_intermediate_result(eval_count, parameters, mean, std):\n",
    "        # callable to store results\n",
    "        counts.append(eval_count)\n",
    "        values.append(mean)\n",
    "\n",
    "    def objective_value(x, hamiltonian):\n",
    "        # get objective value for a given computational basis state\n",
    "\n",
    "        qc = QuantumCircuit(num_qubits)\n",
    "        for k in range(num_qubits):\n",
    "            if x[k] == 1:\n",
    "                qc.x(k)\n",
    "\n",
    "        estimated_cost = estimator.run(qc, hamiltonian).result().values[0]\n",
    "\n",
    "        return -estimated_cost\n",
    "\n",
    "    def bitfield(n: int, L: int) -> list[int]:\n",
    "        # binary representation as list\n",
    "        result = np.binary_repr(n, L)\n",
    "        return [int(digit) for digit in result]  # [2:] to chop off the \"0b\" part\n",
    "\n",
    "    def sample_most_likely(state_vector: QuasiDistribution | Statevector) -> np.ndarray:\n",
    "        \"\"\"Compute the most likely binary string from the state vector.\n",
    "        Args:\n",
    "            state_vector: State vector or quasi-distribution.\n",
    "\n",
    "        Returns:\n",
    "            Binary string as an array of ints.\n",
    "        \"\"\"\n",
    "        if isinstance(state_vector, QuasiDistribution):\n",
    "            values = list(state_vector.values())\n",
    "        else:\n",
    "            values = state_vector\n",
    "        n = int(np.log2(len(values)))\n",
    "        k = np.argmax(np.abs(values))\n",
    "        x = bitfield(k, n)\n",
    "        x.reverse()\n",
    "        return np.asarray(x)\n",
    "\n",
    "    estimator = Estimator(options={\"shots\": int(NUM_SHOTS)})\n",
    "    sampler = Sampler()\n",
    "    optimizer = COBYLA()\n",
    "    qaoa = QAOA(\n",
    "        sampler, optimizer, reps=qaoa_num_layers, callback=store_intermediate_result\n",
    "    )\n",
    "    start = time.time()\n",
    "    result = qaoa.compute_minimum_eigenvalue(hamiltonian)\n",
    "    solving_time = time.time() - start\n",
    "    x = sample_most_likely(result.eigenstate)\n",
    "\n",
    "    transpiled_circuit = transpile(\n",
    "        result.optimal_circuit,\n",
    "        basis_gates=[\"u\", \"cx\"],\n",
    "        optimization_level=transpilation_options[\"qiskit\"],\n",
    "    )\n",
    "\n",
    "    return (\n",
    "        transpiled_circuit.depth(),\n",
    "        transpiled_circuit.count_ops()[\"cx\"],\n",
    "        solving_time,\n",
    "        x,\n",
    "        objective_value(x, hamiltonian),\n",
    "        counts,\n",
    "        values,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "226095ed-ca4b-4a16-b607-92df4ae5e4f5",
   "metadata": {},
   "source": [
    "The same QAOA with Qiskit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c8ff8262-9ce2-4580-9c6e-3e8587ed10e2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T13:47:54.566118Z",
     "iopub.status.busy": "2024-05-07T13:47:54.565658Z",
     "iopub.status.idle": "2024-05-07T14:00:12.450852Z",
     "shell.execute_reply": "2024-05-07T14:00:12.450181Z"
    }
   },
   "outputs": [],
   "source": [
    "pauli_list = get_classiq_hamiltonian(res)\n",
    "hamiltonian = SparsePauliOp.from_list(pauli_list)\n",
    "\n",
    "(\n",
    "    depth_qiskit,\n",
    "    cx_counts_qiskit,\n",
    "    time_qiskit,\n",
    "    most_probable_state,\n",
    "    cost,\n",
    "    iterations_qiskit,\n",
    "    results_qiskit,\n",
    ") = qiskit_qaoa(hamiltonian, QAOA_NUM_LAYERS, NUM_QUBITS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f6039c-3a05-42cc-bba4-866c14f7be07",
   "metadata": {},
   "source": [
    "## 3. Plotting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8057340b-8176-4d4e-ae1a-f18bcdb06392",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T14:00:12.453993Z",
     "iopub.status.busy": "2024-05-07T14:00:12.453447Z",
     "iopub.status.idle": "2024-05-07T14:00:12.641795Z",
     "shell.execute_reply": "2024-05-07T14:00:12.641147Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7feedc946c50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "classiq_color = \"#F43764\"\n",
    "qiskit_color = \"#6FA4FF\"\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rc(\"savefig\", dpi=300)\n",
    "\n",
    "plt.rcParams[\"axes.linewidth\"] = 1\n",
    "plt.rcParams[\"xtick.major.size\"] = 5\n",
    "plt.rcParams[\"xtick.minor.size\"] = 5\n",
    "plt.rcParams[\"ytick.major.size\"] = 5\n",
    "plt.rcParams[\"ytick.minor.size\"] = 5\n",
    "\n",
    "qiskit_label = f\"qiskit: depth={depth_qiskit} \\n cx-counts={cx_counts_qiskit}\"\n",
    "classiq_label = f\"classiq: depth={depth_classiq},\\n cx-counts={cx_counts_classiq}\"\n",
    "max_classiq_iter = len(interations_classiq)\n",
    "plt.plot(\n",
    "    iterations_qiskit[0:max_classiq_iter],\n",
    "    np.real(results_qiskit[0:max_classiq_iter]),\n",
    "    \"-\",\n",
    "    label=qiskit_label,\n",
    "    linewidth=1.5,\n",
    "    color=qiskit_color,\n",
    ")\n",
    "plt.plot(\n",
    "    interations_classiq[0:max_classiq_iter],\n",
    "    results_classiq[0:max_classiq_iter],\n",
    "    \"-\",\n",
    "    label=classiq_label,\n",
    "    linewidth=1.5,\n",
    "    color=classiq_color,\n",
    ")\n",
    "\n",
    "# plt.ylim(0,90)\n",
    "plt.ylabel(\"Energy\", fontsize=16)\n",
    "plt.xlabel(\"Iteration\", fontsize=16)\n",
    "plt.yticks(fontsize=16)\n",
    "plt.xticks(fontsize=16)\n",
    "plt.legend(loc=\"upper right\", fontsize=16)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
